1. Locate Potential Support Vectors for Faster
Sequential Minimal Optimization
Hansheng Lei, PhD
Assistant Professor
Computer and Information Sciences Department

2. Outline Background and Overview Fisher Discriminant Analysis (FDA)
SVM vs. FDA
Combining FDA and SVM
Experimental Results
Computing Infrastructure at UT Brownsville
Application Projects

3. Classification How to classify this data? w x + b>0 w x + b=0

4. Linear Classifiers f a y x f(x,w,b) = sign(w x + b)
How to classify this data?

5. Linear Classifiers f a y x f(x,w,b) = sign(w x + b)
How to classify this data?

6. a f x y
f(x,w,b) = sign(w x + b)
Linear Classifiers
which is best?

7. Linear SVM

8. Solving the Optimization Problem
Find w and b such that
Φ(w) =½ wTw is minimized;
and for all {(xi ,yi)}: yi (wTxi + b) ≥ 1
Subject to

9. Sequential Minimal Optimization (SMO) John C. Platt, 1998
The algorithm proceeds as follows:
1. Find a Lagrange multiplier α1 that violates KKT conditions for the optimization problem.
2. Pick a second multiplier α2 and optimize the pair (α1,α2).
3. Repeat steps 1 and 2 until convergence.
Heuristics are used to choose the pair of multipliers so as to accelerate the rate of convergence.

10. SVM vs. Fisher Discriminant Analysis
1. Similar Format:

11. SVM vs. Fisher Discriminant Analysis
2. Similar Projection:

12. SVM vs. Fisher Discriminant Analysis
2. Similar Projection:

13. Distribution of Support Vectors (SV)

14. F-SMO = FDA+SMO

15. Experimental Results

16. Experimental Results

17. Experimental Results

18. Experimental Results

19. Experimental Results

20. Computing Infrastructure
Graphics Processing Unit (GPU)
Cluster
Field-programmable gate array (FPGA)
GPU Visualization
Advanced CM Flex Lab

21. FUTURO cluster IBM® iDataPlex 320 Cores @ 2.4Ghz 216 TB Storage
QDR 40Gbps
40 Intel®XeonE5540 nodes
192GB RAM per node max
24 TB RAID per node max
NSF MRI funded

22. Futuro Architecture Design

23. FUTURO

24. FUTURO Gallery

25. GPU Server AMAX® ServMax PSC-2n 940 GPU Cores @ 1.3Ghz
12 CPU 2.8 Ghz
4 teraflops max
80 GB memory max
4 Nvidia®Tesla nodes
2 Intel® Xeon EP 5600
NSF MRI funded

26. FPGA Computing 1.2M logic cells 80K system gates 1.1M flip flops
1.7K 18x18Multipliers
532K Slices
16 Xilinx®Spartan FPGAs
Impluse C supported
NSF LSAMP funded

27. GPU Visualization Dual Nvidia®QuadroPlex 960 Nvidia® CUDA cores
3.73 Teraflops
33.3 Mega Pixels
7680x4320 resolution
16 GB Frame Buffer
3D Stereo
US ED CCRAA funded

28. Computational Science Flex Lab
32 SUN Ultra nodes
Intel® 3.0 Ghz
128 CPU Cores
1024 CUDA Cores
320GB RAM
8.8TB Storage
US ED CCRAA funded

29. Enabled Projects
1. Tracking LIGO Detector Noise for Gravitational Wave Detection (NSF) 2. Genetic Data Analysis in Complex Human Diseases (University of Texas Health Science Center) 3. Dynamical Systems and Stellar Populations(NASA) 4. Collaborative Filtering using Multispectral Information(*) 5. Visualization of High-dimensional Data (NSF pending) 6. Practical Algorithms for the Subgraph Isomorphism Problem

30. Subproject 1– Parallel and Distributed Clustering
Tracking LIGO Detector Noise for Gravitational Wave Detection (PI: Lei, Tang, Mukherjee, Mohanty, co-PI: Iglesias)
Subproject 1– Parallel and Distributed Clustering
Subproject 2 – Parallel and Distributed Classification
Subproject 3: Parallel and Distributed Rule Discovery
Computing infrastructure   and distributed KDD research.

31. Genetic Data Analysis in Complex Human Diseases (PI: Figueroa)

32. Visualization of High-dimensional Data (PI: Quweider , co-PI: Mukherjee, Mohanty)
Visualization Framework.

33. Application Projects Automated optical inspection (AOI)
Special Sound Detection,

34. Automated Optical Inspection

35. AOI components Computer vision software
Machine vision hardware for data acquisition, e.g.. CCD camera and optical lens, or X-ray,
Auto control system
Illumination system
Optimal AOI, Viking Test Ltd

36. Special Sound Detection

37. The End Welcome to Visit UTB